Non-deployment factors affecting psychological wellbeing in military personnel: literature review
Abstract: Background: Most military mental health research focuses on the impact of deployment-related stress; less is known about how everyday work-related factors affect wellbeing. Aims: This systematic narrative literature review aimed to identify non-deployment-related factors contributing to the wellbeing of military personnel. Method: Electronic literature databases were searched and the findings of relevant studies were used to explore non-deployment-related risk and resilience factors. Results: Fifty publications met the inclusion criteria. Determinants of non-deployment stress were identified as: relationships with others (including leadership/supervisory support; social support/cohesion; harassment/discrimination) and role-related stressors (role conflict; commitment and effort-reward imbalance; work overload/job demands; family-related issues/work-life balance; and other factors including control/autonomy, physical work environment and financial strain). Factors positively impacting wellbeing (such as exercise) were also identified. Conclusions: The literature suggests that non-deployment stressors present a significant occupational health hazard in routine military environments and interpersonal relationships at work are of fundamental importance. Findings suggest that in order to protect the wellbeing of personnel and improve performance, military organisations should prioritise strengthening relationships between employees and their supervisors/colleagues. Recommendations for addressing these stressors in British military personnel were developed.
Abstract: Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress notes, can be mined for clinically relevant information, including the presence of substance use and relapse-critical markers of risk and recovery from opioid use disorder (OUD). In this study, we used natural language processing (NLP) to automate the extraction of opioid relapses, and the timing of these occurrences, from veteran patients' electronic medical record. We then demonstrated the utility of our NLP tool via analysis of pre-/post-COVID-19 opioid relapse trends among veterans with OUD. For this demonstration, we analyzed data from 107,606 veterans OUD enrolled in Veterans Health Administration, comparing a pandemic-exposed cohort (n = 53,803; January 2019-March 2021) to a matched prepandemic cohort (n = 53,803; October 2017-December 2019). The recall of our NLP tool was 75% and our precision was 94%, demonstrating moderate sensitivity and excellent specificity. Using the NLP tool, we found that the odds of opioid relapse postpandemic onset were proportionally higher compared to prepandemic trends, despite patients having fewer mental health encounters from which to derive instances of relapse postpandemic onset. In this research application of the tool, and as hypothesized, we found that opioid relapse risk was elevated postpandemic. The application of NLP Methods: to identify and monitor relapse risk holds promise for future surveillance, risk prevention, and clinical outcome research.